{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from datasets import MultivariateNormalDataset\n",
    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
    "num_gpus = 1 if device=='cuda' else 0\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 78.83batch/s, batch_idx=5, gpu=0, loss=-1.788, loss_tqdm=-1.8, mi=1.8, v_num=868]   \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 228.25batch/s]\n",
      "True_mi 1.9585182666778564\n",
      "MINE 1.7592424154281616\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 68.66batch/s, batch_idx=5, gpu=0, loss=-0.634, loss_tqdm=-.608, mi=0.608, v_num=869]\n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 217.39batch/s]\n",
      "True_mi 0.6366140246391296\n",
      "MINE 0.5953158140182495\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 77.89batch/s, batch_idx=5, gpu=0, loss=-0.328, loss_tqdm=-.41, mi=0.41, v_num=870]    \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 221.10batch/s]\n",
      "True_mi 0.34662458300590515\n",
      "MINE 0.33796820044517517\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 77.44batch/s, batch_idx=5, gpu=0, loss=-0.203, loss_tqdm=-.219, mi=0.219, v_num=871]  \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 219.99batch/s]\n",
      "True_mi 0.19285525381565094\n",
      "MINE 0.17817780375480652\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 74.54batch/s, batch_idx=5, gpu=0, loss=-0.095, loss_tqdm=-.0952, mi=0.0952, v_num=872]\n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 228.47batch/s]\n",
      "True_mi 0.09923666715621948\n",
      "MINE 0.10427713394165039\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 69.14batch/s, batch_idx=5, gpu=0, loss=-0.050, loss_tqdm=-.0628, mi=0.0628, v_num=873]  \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 217.35batch/s]\n",
      "True_mi 0.041695233434438705\n",
      "MINE 0.0624389611184597\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 75.23batch/s, batch_idx=5, gpu=0, loss=-0.013, loss_tqdm=-.0228, mi=0.0228, v_num=874]    \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 221.41batch/s]\n",
      "True_mi 0.0101024080067873\n",
      "MINE 0.01829810068011284\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 75.89batch/s, batch_idx=5, gpu=0, loss=-0.004, loss_tqdm=-.00226, mi=0.00226, v_num=875]  \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 220.85batch/s]\n",
      "True_mi -0.0\n",
      "MINE 0.00547037273645401\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 77.36batch/s, batch_idx=5, gpu=0, loss=-0.015, loss_tqdm=-.0232, mi=0.0232, v_num=876]    \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 78.92batch/s]\n",
      "True_mi 0.0101024080067873\n",
      "MINE 0.013945117592811584\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 78.98batch/s, batch_idx=5, gpu=0, loss=-0.041, loss_tqdm=-.0236, mi=0.0236, v_num=877]    \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 229.64batch/s]\n",
      "True_mi 0.041695233434438705\n",
      "MINE 0.03708716109395027\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 78.52batch/s, batch_idx=5, gpu=0, loss=-0.109, loss_tqdm=-.117, mi=0.117, v_num=878]    \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 212.03batch/s]\n",
      "True_mi 0.09923666715621948\n",
      "MINE 0.10367246717214584\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 80.68batch/s, batch_idx=5, gpu=0, loss=-0.193, loss_tqdm=-.16, mi=0.16, v_num=879]     \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 217.36batch/s]\n",
      "True_mi 0.19285525381565094\n",
      "MINE 0.18928179144859314\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 51.81batch/s, batch_idx=5, gpu=0, loss=-0.341, loss_tqdm=-.367, mi=0.367, v_num=880] \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 172.57batch/s]\n",
      "True_mi 0.34662458300590515\n",
      "MINE 0.32912957668304443\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 72.38batch/s, batch_idx=5, gpu=0, loss=-0.624, loss_tqdm=-.554, mi=0.554, v_num=881]  \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 204.85batch/s]\n",
      "True_mi 0.6366140246391296\n",
      "MINE 0.6471976637840271\n",
      "Epoch 200: 100%|██████████| 6/6 [00:00<00:00, 75.76batch/s, batch_idx=5, gpu=0, loss=-1.823, loss_tqdm=-1.8, mi=1.8, v_num=882]   \n",
      "Testing: 100%|██████████| 6/6 [00:00<00:00, 220.10batch/s]\n",
      "True_mi 1.9585182666778564\n",
      "MINE 1.76433265209198\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from models.mine import MutualInformationEstimator\n",
    "from pytorch_lightning import Trainer\n",
    "import logging\n",
    "logging.getLogger().setLevel(logging.ERROR)\n",
    "\n",
    "dim = 1\n",
    "N = 3000\n",
    "lr = 1e-4\n",
    "epochs = 200\n",
    "batch_size = 500\n",
    "\n",
    "steps = 15\n",
    "rhos = np.linspace(-0.99, 0.99, steps)\n",
    "loss_type = ['mine_biased']\n",
    "\n",
    "results_dict = dict()\n",
    "\n",
    "for loss in loss_type:\n",
    "    results = []\n",
    "    for rho in rhos:\n",
    "        train_loader = torch.utils.data.DataLoader(\n",
    "        MultivariateNormalDataset(N, dim, rho), batch_size=batch_size, shuffle=True)\n",
    "        test_loader = torch.utils.data.DataLoader(\n",
    "        MultivariateNormalDataset(N, dim, rho), batch_size=batch_size, shuffle=True)\n",
    "\n",
    "        \n",
    "        true_mi = train_loader.dataset.true_mi\n",
    "\n",
    "        kwargs = {\n",
    "            'lr': lr,\n",
    "            'batch_size': batch_size,\n",
    "            'train_loader': train_loader,\n",
    "            'test_loader': test_loader,\n",
    "            'alpha': 1.0\n",
    "        }\n",
    "\n",
    "        model = MutualInformationEstimator(\n",
    "            dim, dim, loss=loss, **kwargs).to(device)\n",
    "        \n",
    "        trainer = Trainer(max_epochs=epochs, early_stop_callback=False, gpus=num_gpus)\n",
    "        trainer.fit(model)\n",
    "        trainer.test()\n",
    "\n",
    "        print(\"True_mi {}\".format(true_mi))\n",
    "        print(\"MINE {}\".format(model.avg_test_mi))\n",
    "        results.append((rho, model.avg_test_mi, true_mi))\n",
    "\n",
    "    results = np.array(results)\n",
    "    results_dict[loss] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/ipykernel_launcher.py:11: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, len(loss_type), sharex = True, figsize = (6,4))\n",
    "plots = []\n",
    "for ix, loss in enumerate(loss_type):\n",
    "    results = results_dict[loss]\n",
    "    plots += axs.plot(results[:,0], results[:,1], label='MINE')\n",
    "    plots += axs.plot(results[:,0], results[:,2], linestyle='--', label='True MI')\n",
    "    axs.set_xlabel('correlation')\n",
    "    axs.set_ylabel('mi')\n",
    "    #axs.title.set_text(f\"{loss} for {dim} dimensional inputs\")\n",
    "    \n",
    "fig.legend(plots[0:2], labels = ['MINE', 'True MI'], loc='upper right')\n",
    "fig.savefig('figures/mi_estimation.png')\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experiment: 1, Sigma: 0.0...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 92.53batch/s, batch_idx=11, gpu=0, loss=-4.760, loss_tqdm=-3.92, mi=3.92, v_num=883] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 532.19batch/s]\n",
      "Experiment: 2, Sigma: 0.0...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 106.25batch/s, batch_idx=11, gpu=0, loss=-2.323, loss_tqdm=-2.51, mi=2.51, v_num=884]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 585.62batch/s]\n",
      "Experiment: 3, Sigma: 0.0...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 101.98batch/s, batch_idx=11, gpu=0, loss=-4.378, loss_tqdm=-6.14, mi=6.14, v_num=885] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 533.74batch/s]\n",
      "Experiment: 1, Sigma: 0.09999999403953552...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 102.18batch/s, batch_idx=11, gpu=0, loss=-3.130, loss_tqdm=-3.58, mi=3.58, v_num=886] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 570.89batch/s]\n",
      "Experiment: 2, Sigma: 0.09999999403953552...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.26batch/s, batch_idx=11, gpu=0, loss=-1.760, loss_tqdm=-1.9, mi=1.9, v_num=887]    \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 583.09batch/s]\n",
      "Experiment: 3, Sigma: 0.09999999403953552...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 99.05batch/s, batch_idx=11, gpu=0, loss=-2.929, loss_tqdm=-2.63, mi=2.63, v_num=888]    \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 480.40batch/s]\n",
      "Experiment: 1, Sigma: 0.19999998807907104...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 100.72batch/s, batch_idx=11, gpu=0, loss=-2.008, loss_tqdm=-2.07, mi=2.07, v_num=889] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 552.98batch/s]\n",
      "Experiment: 2, Sigma: 0.19999998807907104...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 101.16batch/s, batch_idx=11, gpu=0, loss=-1.161, loss_tqdm=-1.23, mi=1.23, v_num=890]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 536.72batch/s]\n",
      "Experiment: 3, Sigma: 0.19999998807907104...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 91.67batch/s, batch_idx=11, gpu=0, loss=-1.865, loss_tqdm=-1.65, mi=1.65, v_num=891]   \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 556.36batch/s]\n",
      "Experiment: 1, Sigma: 0.29999998211860657...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.76batch/s, batch_idx=11, gpu=0, loss=-1.443, loss_tqdm=-1.27, mi=1.27, v_num=892] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 576.95batch/s]\n",
      "Experiment: 2, Sigma: 0.29999998211860657...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 105.50batch/s, batch_idx=11, gpu=0, loss=-0.765, loss_tqdm=-.794, mi=0.794, v_num=893]\n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 580.54batch/s]\n",
      "Experiment: 3, Sigma: 0.29999998211860657...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 105.64batch/s, batch_idx=11, gpu=0, loss=-1.316, loss_tqdm=-1.27, mi=1.27, v_num=894] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 580.92batch/s]\n",
      "Experiment: 1, Sigma: 0.3999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 99.22batch/s, batch_idx=11, gpu=0, loss=-1.070, loss_tqdm=-.73, mi=0.73, v_num=895]   \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 487.55batch/s]\n",
      "Experiment: 2, Sigma: 0.3999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 105.36batch/s, batch_idx=11, gpu=0, loss=-0.533, loss_tqdm=-.522, mi=0.522, v_num=896]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 587.42batch/s]\n",
      "Experiment: 3, Sigma: 0.3999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.54batch/s, batch_idx=11, gpu=0, loss=-0.977, loss_tqdm=-.975, mi=0.975, v_num=897]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 522.67batch/s]\n",
      "Experiment: 1, Sigma: 0.4999999701976776...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 74.91batch/s, batch_idx=11, gpu=0, loss=-0.840, loss_tqdm=-.912, mi=0.912, v_num=898] \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 578.11batch/s]\n",
      "Experiment: 2, Sigma: 0.4999999701976776...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.10batch/s, batch_idx=11, gpu=0, loss=-0.401, loss_tqdm=-.406, mi=0.406, v_num=899]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 581.77batch/s]\n",
      "Experiment: 3, Sigma: 0.4999999701976776...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.66batch/s, batch_idx=11, gpu=0, loss=-0.706, loss_tqdm=-.638, mi=0.638, v_num=900]\n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 582.29batch/s]\n",
      "Experiment: 1, Sigma: 0.5999999642372131...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 105.33batch/s, batch_idx=11, gpu=0, loss=-0.653, loss_tqdm=-.81, mi=0.81, v_num=901]    \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 578.79batch/s]\n",
      "Experiment: 2, Sigma: 0.5999999642372131...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 106.67batch/s, batch_idx=11, gpu=0, loss=-0.276, loss_tqdm=-.375, mi=0.375, v_num=902]   \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 590.22batch/s]\n",
      "Experiment: 3, Sigma: 0.5999999642372131...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 104.87batch/s, batch_idx=11, gpu=0, loss=-0.555, loss_tqdm=-.538, mi=0.538, v_num=903]\n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 580.15batch/s]\n",
      "Experiment: 1, Sigma: 0.6999999284744263...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 92.50batch/s, batch_idx=11, gpu=0, loss=-0.538, loss_tqdm=-.384, mi=0.384, v_num=904]   \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 564.00batch/s]\n",
      "Experiment: 2, Sigma: 0.6999999284744263...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 91.74batch/s, batch_idx=11, gpu=0, loss=-0.214, loss_tqdm=-.322, mi=0.322, v_num=905]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 542.17batch/s]\n",
      "Experiment: 3, Sigma: 0.6999999284744263...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 100.21batch/s, batch_idx=11, gpu=0, loss=-0.436, loss_tqdm=-.404, mi=0.404, v_num=906]\n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 552.44batch/s]\n",
      "Experiment: 1, Sigma: 0.7999999523162842...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 95.80batch/s, batch_idx=11, gpu=0, loss=-0.428, loss_tqdm=-.531, mi=0.531, v_num=907]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 545.73batch/s]\n",
      "Experiment: 2, Sigma: 0.7999999523162842...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 90.73batch/s, batch_idx=11, gpu=0, loss=-0.180, loss_tqdm=-.202, mi=0.202, v_num=908]     \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 566.89batch/s]\n",
      "Experiment: 3, Sigma: 0.7999999523162842...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 90.09batch/s, batch_idx=11, gpu=0, loss=-0.354, loss_tqdm=-.459, mi=0.459, v_num=909]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 573.13batch/s]\n",
      "Experiment: 1, Sigma: 0.8999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 90.23batch/s, batch_idx=11, gpu=0, loss=-0.348, loss_tqdm=-.2, mi=0.2, v_num=910]      \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 540.53batch/s]\n",
      "Experiment: 2, Sigma: 0.8999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 91.24batch/s, batch_idx=11, gpu=0, loss=-0.142, loss_tqdm=-.19, mi=0.19, v_num=911]      \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 541.78batch/s]\n",
      "Experiment: 3, Sigma: 0.8999999761581421...\n",
      "Epoch 200: 100%|██████████| 12/12 [00:00<00:00, 89.73batch/s, batch_idx=11, gpu=0, loss=-0.294, loss_tqdm=-.254, mi=0.254, v_num=912]  \n",
      "Testing: 100%|██████████| 12/12 [00:00<00:00, 486.64batch/s]\n"
     ]
    }
   ],
   "source": [
    "## Function experiment\n",
    "N = 3000\n",
    "lr = 1e-4\n",
    "batch_size = 256\n",
    "epochs = 200\n",
    "\n",
    "def f1(x): return x\n",
    "def f2(x): return x**3\n",
    "def f3(x): return torch.sin(x)\n",
    "\n",
    "sigmas = torch.linspace(0, 0.9, 10)\n",
    "fs = [f1, f2, f3]\n",
    "dim = 2\n",
    "\n",
    "res = []\n",
    "for sigma in sigmas:\n",
    "    for ix, f in enumerate(fs):\n",
    "        print(f\"Experiment: {ix + 1}, Sigma: {sigma}...\")\n",
    "\n",
    "        kwargs = {\n",
    "            'N': N,\n",
    "            'sigma': sigma,\n",
    "            'f': f,\n",
    "            'lr': lr,\n",
    "            'batch_size': batch_size,\n",
    "            'alpha': 0.1\n",
    "        }\n",
    "\n",
    "        model = MutualInformationEstimator(\n",
    "            dim, dim, loss='mine_biased', **kwargs).to(device)\n",
    "        trainer = Trainer(max_epochs=epochs,\n",
    "                          early_stop_callback=False, gpus=num_gpus)\n",
    "        trainer.fit(model)\n",
    "        trainer.test()\n",
    "\n",
    "        # Append result\n",
    "        res.append([ix, sigma, model.avg_test_mi])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = np.array(res)\n",
    "Z = res[:, -1].reshape((len(sigmas), len(fs))).T\n",
    "plt.figure()\n",
    "plt.imshow(Z, cmap='Blues')\n",
    "plt.yticks([0,1,2],['x', 'x³', 'sin(x)'])\n",
    "plt.xticks(np.arange(10),sigmas.numpy().round(2))\n",
    "plt.xlabel('sigma')\n",
    "plt.ylabel('f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## GANs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n",
      "Epoch 600: 100%|██████████| 16/16 [00:00<00:00, 29.87batch/s, batch_idx=15, d_loss=0.645, g_loss=0.918, gpu=0, loss=0.767, v_num=956]\n",
      "Epoch 600: 100%|██████████| 16/16 [00:00<00:00, 29.28batch/s, batch_idx=15, d_loss=0.647, g_loss=-2.01, gpu=0, loss=-0.810, v_num=957]\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "# GAN spiral experiment\n",
    "from models.gan import GAN\n",
    "from models.mine import T, Mine\n",
    "from datasets import load_dataloader\n",
    "from pytorch_lightning import Trainer\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "datasets = ['gaussians']\n",
    "loss = 'mine_biased'\n",
    "num_samples = 4000\n",
    "batch_size = 256\n",
    "epochs = 600\n",
    "lr = 1e-4\n",
    "betas = [0.0, 1.0]\n",
    "alpha = 0.1\n",
    "#dataset = 'spiral'\n",
    "\n",
    "results = defaultdict(dict)\n",
    "\n",
    "for dataset in datasets:\n",
    "    for beta in betas:\n",
    "        train_loader = load_dataloader(dataset, batch_size, N=num_samples)\n",
    "        img, label = next(iter(train_loader))\n",
    "\n",
    "        output_dim = np.prod(img.shape[1:])\n",
    "        input_dim = 100\n",
    "        lr = lr\n",
    "        beta = beta\n",
    "\n",
    "        kwargs = {\n",
    "            'lr': lr,\n",
    "            'train_loader': train_loader,\n",
    "            'beta': beta,\n",
    "            'alpha': alpha,\n",
    "            'use_conditional': True,\n",
    "            'condition_on_labels': True\n",
    "        }\n",
    "\n",
    "        mi_model = T(output_dim, label.shape[-1]).to(device)\n",
    "        mi_estimator = Mine(mi_model, loss=loss).to(device)\n",
    "\n",
    "        model = GAN(input_dim, \n",
    "                    output_dim, \n",
    "                    device=device,\n",
    "                   generator_type = 'linear', \n",
    "                    discriminator_type = 'linear', \n",
    "                    mi_estimator=mi_estimator, \n",
    "                    conditional_dim=label.shape[-1], **kwargs).to(device)\n",
    "\n",
    "        trainer = Trainer(max_epochs=epochs, early_stop_callback = False, gpus=num_gpus)\n",
    "        trainer.fit(model)\n",
    "\n",
    "        generated_grid, conditional = model.generate_img_grid(num_samples)\n",
    "        results[dataset][beta] = [generated_grid.cpu().data.numpy(), conditional.cpu().data.numpy()]\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datasets import to_onehot\n",
    "\n",
    "data = train_loader.dataset\n",
    "x_np = data.x_np\n",
    "labels = data.labels\n",
    "\n",
    "plt.figure()\n",
    "plt.scatter(x_np[:,0], x_np[:,1], c = labels)\n",
    "plt.suptitle('Original')\n",
    "plt.savefig('figures/25gaussians.png')\n",
    "\n",
    "dataset = 'gaussians'\n",
    "generated_grid, conditional = results[dataset][0.0]\n",
    "model.plot_grid(generated_grid, conditional)\n",
    "title = plt.suptitle('Vanilla GAN')\n",
    "plt.savefig('figures/25gaussians_gan.png')\n",
    "\n",
    "#plt.figure()\n",
    "generated_grid, conditional = results[dataset][1.0]\n",
    "model.plot_grid(generated_grid, conditional)\n",
    "title = plt.suptitle('Regularizing with mutual information')\n",
    "plt.savefig('figures/25gaussians_mine.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "## IBNetwork on MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[autoreload of models.mine failed: Traceback (most recent call last):\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 245, in check\n",
      "    superreload(m, reload, self.old_objects)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 410, in superreload\n",
      "    update_generic(old_obj, new_obj)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
      "    update(a, b)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 302, in update_class\n",
      "    if update_generic(old_obj, new_obj): continue\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
      "    update(a, b)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 302, in update_class\n",
      "    if update_generic(old_obj, new_obj): continue\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
      "    update(a, b)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 302, in update_class\n",
      "    if update_generic(old_obj, new_obj): continue\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 347, in update_generic\n",
      "    update(a, b)\n",
      "  File \"/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/IPython/extensions/autoreload.py\", line 302, in update_class\n",
      "    if update_generic(old_obj, new_obj): continue\n",
      "RecursionError: maximum recursion depth exceeded\n",
      "]\n",
      "Validation sanity check: 100%|██████████| 5/5 [00:00<00:00, 42.39batch/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Device: cuda\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r",
      "                                                                         "
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:   1%|          | 3/275 [00:00<00:15, 17.90batch/s, accuracy=0.211, batch_idx=2, error_rate=0.789, gpu=0, loss=2.304, v_num=959]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\r"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1:  85%|████████▌ | 235/275 [00:10<00:01, 23.19batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Validating:   0%|          | 0/40 [00:00<?, ?batch/s]\u001b[A\n",
      "Epoch 1:  87%|████████▋ | 240/275 [00:10<00:01, 26.64batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  89%|████████▉ | 245/275 [00:10<00:01, 29.92batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  91%|█████████ | 250/275 [00:11<00:00, 32.80batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  93%|█████████▎| 255/275 [00:11<00:00, 35.09batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  95%|█████████▍| 260/275 [00:11<00:00, 36.80batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  96%|█████████▋| 265/275 [00:11<00:00, 38.16batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1:  98%|█████████▊| 270/275 [00:11<00:00, 39.13batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 1: 100%|██████████| 275/275 [00:11<00:00, 40.24batch/s, accuracy=0.927, batch_idx=234, error_rate=0.0729, gpu=0, loss=0.178, v_num=959]\n",
      "Epoch 2:   1%|          | 2/275 [00:00<00:06, 40.24batch/s, accuracy=0.965, batch_idx=1, error_rate=0.0352, gpu=0, loss=0.178, v_num=959]    "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gustaf/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_io.py:342: UserWarning: Did not find hyperparameters at model.hparams. Saving checkpoint without hyperparameters\n",
      "  \"Did not find hyperparameters at model.hparams. Saving checkpoint without\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2:  56%|█████▌    | 154/275 [00:06<00:05, 22.88batch/s, accuracy=0.938, batch_idx=153, error_rate=0.0625, gpu=0, loss=0.118, v_num=959] "
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-122-38528de563b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     18\u001b[0m trainer = Trainer(max_epochs=epochs,\n\u001b[1;32m     19\u001b[0m                   gpus=1, early_stop_callback=False)\n\u001b[0;32m---> 20\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mibnetwork\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    694\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 695\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msingle_gpu_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    696\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    697\u001b[0m         \u001b[0;31m# ON CPU\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/distrib_parts.py\u001b[0m in \u001b[0;36msingle_gpu_train\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    439\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_pretrain_routine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdp_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_pretrain_routine\u001b[0;34m(self, model)\u001b[0m\n\u001b[1;32m    827\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    828\u001b[0m         \u001b[0;31m# CORE TRAINING LOOP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 829\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    330\u001b[0m             \u001b[0;31m# RUN TNG EPOCH\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m             \u001b[0;31m# -----------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m             \u001b[0;31m# update LR schedulers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    384\u001b[0m             \u001b[0;31m# RUN TRAIN STEP\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0;31m# ---------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 386\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    387\u001b[0m             \u001b[0mbatch_result\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_norm_dic\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_step_metrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m    504\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m                 \u001b[0;31m# calculate loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m                 \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer_closure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m                 \u001b[0;31m# nan grads\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_closure\u001b[0;34m()\u001b[0m\n\u001b[1;32m    473\u001b[0m                     \u001b[0;31m# forward pass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    474\u001b[0m                     output = self.training_forward(\n\u001b[0;32m--> 475\u001b[0;31m                         split_batch, batch_idx, opt_idx, self.hiddens)\n\u001b[0m\u001b[1;32m    476\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    477\u001b[0m                     \u001b[0mclosure_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mtraining_forward\u001b[0;34m(self, batch, batch_idx, opt_idx, hiddens)\u001b[0m\n\u001b[1;32m    591\u001b[0m             \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransfer_batch_to_gpu\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgpu_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m         \u001b[0;31m# CPU forward\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fun/mine/models/information_bottleneck.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, batch, batch_idx)\u001b[0m\n\u001b[1;32m     92\u001b[0m             \u001b[0mimg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mimg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     93\u001b[0m             \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 94\u001b[0;31m         \u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     95\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     96\u001b[0m         \u001b[0maccuracy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmisclass_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_stats\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fun/mine/models/information_bottleneck.py\u001b[0m in \u001b[0;36mloss_fn\u001b[0;34m(self, img, label)\u001b[0m\n\u001b[1;32m     82\u001b[0m         \u001b[0mencoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdecoded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mce_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mce_loss_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdecoded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 84\u001b[0;31m         \u001b[0mmi_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmi_estimator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencoded\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mce_loss\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbeta\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mmi_loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fun/mine/models/mine.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x, z, z_marg)\u001b[0m\n\u001b[1;32m    109\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m         \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m         \u001b[0mt_marg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz_marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'mine'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fun/mine/models/layers.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *input)\u001b[0m\n\u001b[1;32m     20\u001b[0m                 \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m                 \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/fun/mine/models/information_bottleneck.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/container.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     90\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     91\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmodule\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_modules\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 92\u001b[0;31m             \u001b[0minput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     93\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     94\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    539\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    540\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    542\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mhook\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0mhook_result\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/modules/linear.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m     85\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     88\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     89\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mextra_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mlinear\u001b[0;34m(input, weight, bias)\u001b[0m\n\u001b[1;32m   1368\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mbias\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m         \u001b[0;31m# fused op is marginally faster\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1370\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1371\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1372\u001b[0m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatmul\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mformat_stack\u001b[0;34m(f, limit)\u001b[0m\n\u001b[1;32m    195\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mf\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mf_back\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mformat_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextract_stack\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlimit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mformat_list\u001b[0;34m(extracted_list)\u001b[0m\n\u001b[1;32m     37\u001b[0m     \u001b[0mwhose\u001b[0m \u001b[0msource\u001b[0m \u001b[0mtext\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \"\"\"\n\u001b[0;32m---> 39\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mStackSummary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextracted_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/fun/lib/python3.6/traceback.py\u001b[0m in \u001b[0;36mformat\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    413\u001b[0m                 \u001b[0mlast_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    414\u001b[0m                 \u001b[0mlast_line\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlineno\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 415\u001b[0;31m                 \u001b[0mlast_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mframe\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    416\u001b[0m                 \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m             \u001b[0mcount\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from models.information_bottleneck import IBNetwork, StatisticsNetwork\n",
    "from models.mine import Mine\n",
    "\n",
    "batch_size = 512\n",
    "epochs = 100\n",
    "K = 256\n",
    "beta = 1e-3\n",
    "lr = 1e-3\n",
    "x_dim = 28*28\n",
    "z_dim = K\n",
    "\n",
    "\n",
    "t = StatisticsNetwork(x_dim, z_dim, device=device).to(device)\n",
    "mi_estimator = Mine(t, loss='mine', method='concat').to(device)\n",
    "ibnetwork = IBNetwork(input_dim=28*28, K=K, output_dim=10,\n",
    "                      mi_estimator=mi_estimator, lr=lr, beta=beta).to(device)\n",
    "\n",
    "trainer = Trainer(max_epochs=epochs,\n",
    "                  gpus=1, early_stop_callback=False)\n",
    "trainer.fit(ibnetwork)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:fun] *",
   "language": "python",
   "name": "conda-env-fun-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
